
# Installing the libraries with the specified version.
# uncomment and run the following line if Google Colab is being used
# !pip install scikit-learn==1.2.2 seaborn==0.13.1 matplotlib==3.7.1 numpy==1.25.2 pandas==1.5.3 yellowbrick==1.5 -q --user


# Installing the libraries with the specified version.
# uncomment and run the following lines if Jupyter Notebook is being used
# !pip install scikit-learn==1.2.2 seaborn==0.13.1 matplotlib==3.7.1 numpy==1.25.2 pandas==1.5.2 yellowbrick==1.5 -q --user
# !pip install --upgrade -q jinja2


# To suppress the warnings
import warnings

warnings.filterwarnings("ignore")

# Libraries to help with reading and manipulating data
import numpy as np
import pandas as pd

# Libraries to help with data visualization
import matplotlib.pyplot as plt
import seaborn as sns

# to scale the data using z-score
from sklearn.preprocessing import StandardScaler

# to compute distances
from scipy.spatial.distance import cdist

# to perform k-means clustering and compute silhouette scores
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score

# to visualize the elbow curve and silhouette scores
from yellowbrick.cluster import KElbowVisualizer, SilhouetteVisualizer

# to compute distances
from scipy.spatial.distance import pdist

# to perform hierarchical clustering, compute cophenetic correlation, and create dendrograms
from sklearn.cluster import AgglomerativeClustering
from scipy.cluster.hierarchy import dendrogram, linkage, cophenet

# to perform PCA
from sklearn.decomposition import PCA


# mount your google drive
from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive


# loading the dataset
path = "/content/drive/MyDrive/UTAS/8Unsupervised_Learning/Project/stock_data.csv"
data = pd.read_csv(path)


# shape of the dataset
data.shape

(340, 15)


# look at random sample of 10 rows
data.sample(n=10, random_state=1)


# data types for original dataset
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 340 entries, 0 to 339
Data columns (total 15 columns):
 #   Column                        Non-Null Count  Dtype  
---  ------                        --------------  -----  
 0   Ticker Symbol                 340 non-null    object 
 1   Security                      340 non-null    object 
 2   GICS Sector                   340 non-null    object 
 3   GICS Sub Industry             340 non-null    object 
 4   Current Price                 340 non-null    float64
 5   Price Change                  340 non-null    float64
 6   Volatility                    340 non-null    float64
 7   ROE                           340 non-null    int64  
 8   Cash Ratio                    340 non-null    int64  
 9   Net Cash Flow                 340 non-null    int64  
 10  Net Income                    340 non-null    int64  
 11  Earnings Per Share            340 non-null    float64
 12  Estimated Shares Outstanding  340 non-null    float64
 13  P/E Ratio                     340 non-null    float64
 14  P/B Ratio                     340 non-null    float64
dtypes: float64(7), int64(4), object(4)
memory usage: 40.0+ KB


# create a copy of the original data to avoid any changes to original dataset
df=data.copy()


# rename the columns by replacing spaces with underscore
df.columns=[c.replace(" ", "_") for c in df.columns]


# statistical summary for original dataset
df.describe(include='all').T


#Look at all unique values for `GICS_Sector`
df['GICS_Sector'].unique()

array(['Industrials', 'Health Care', 'Information Technology',
       'Consumer Staples', 'Utilities', 'Financials', 'Real Estate',
       'Materials', 'Consumer Discretionary', 'Energy',
       'Telecommunications Services'], dtype=object)


#Look at all unique values for `GICS_Sector`
df['GICS_Sub_Industry'].unique()

array(['Airlines', 'Pharmaceuticals', 'Health Care Equipment',
       'Application Software', 'Semiconductors', 'Agricultural Products',
       'Data Processing & Outsourced Services', 'MultiUtilities',
       'Electric Utilities', 'Life & Health Insurance',
       'Property & Casualty Insurance', 'REITs', 'Multi-line Insurance',
       'Insurance Brokers', 'Internet Software & Services',
       'Specialty Chemicals', 'Building Products', 'Biotechnology',
       'Semiconductor Equipment', 'Electrical Components & Equipment',
       'Asset Management & Custody Banks', 'Specialized REITs',
       'Internet & Direct Marketing Retail', 'Specialty Stores',
       'Managed Health Care', 'Oil & Gas Exploration & Production',
       'Electronic Components', 'Aerospace & Defense',
       'Home Entertainment Software', 'Residential REITs',
       'Water Utilities', 'Consumer Finance', 'Banks',
       'Oil & Gas Equipment & Services', 'Metal & Glass Containers',
       'Health Care Distributors', 'Auto Parts & Equipment',
       'Construction & Farm Machinery & Heavy Trucks',
       'Real Estate Services', 'Hotels, Resorts & Cruise Lines',
       'Fertilizers & Agricultural Chemicals', 'Regional Banks',
       'Household Products', 'Integrated Oil & Gas',
       'Air Freight & Logistics', 'Cable & Satellite',
       'Financial Exchanges & Data', 'Restaurants',
       'Industrial Machinery', 'Health Care Supplies', 'Railroads',
       'Integrated Telecommunications Services',
       'IT Consulting & Other Services', 'Drug Retail',
       'Diversified Chemicals', 'Health Care Facilities',
       'Industrial Conglomerates', 'Broadcasting & Cable TV',
       'Research & Consulting Services', 'Soft Drinks',
       'Investment Banking & Brokerage', 'Automobile Manufacturers',
       'Copper', 'Electronic Equipment & Instruments',
       'Diversified Commercial Services', 'Retail REITs',
       'Consumer Electronics', 'Tires & Rubber', 'Industrial Materials',
       'Leisure Products', 'Motorcycle Manufacturers',
       'Technology Hardware, Storage & Peripherals', 'Computer Hardware',
       'Packaged Foods & Meats', 'Paper Packaging', 'Advertising',
       'Trucking', 'Networking Equipment',
       'Oil & Gas Refining & Marketing & Transportation', 'Homebuilding',
       'Distributors', 'Multi-Sector Holdings', 'Alternative Carriers',
       'Diversified Financial Services', 'Home Furnishings',
       'Construction Materials', 'Tobacco',
       'Life Sciences Tools & Services', 'Gold', 'Steel',
       'Housewares & Specialties', 'Thrifts & Mortgage Finance',
       'Technology, Hardware, Software and Supplies', 'Personal Products',
       'Industrial Gases', 'Human Resource & Employment Services',
       'Office REITs', 'Brewers', 'Publishing', 'Specialty Retail',
       'Apparel, Accessories & Luxury Goods', 'Household Appliances',
       'Environmental Services', 'Casinos & Gaming'], dtype=object)


# check for missing values
df.isnull().sum()

Ticker_Symbol                   0
Security                        0
GICS_Sector                     0
GICS_Sub_Industry               0
Current_Price                   0
Price_Change                    0
Volatility                      0
ROE                             0
Cash_Ratio                      0
Net_Cash_Flow                   0
Net_Income                      0
Earnings_Per_Share              0
Estimated_Shares_Outstanding    0
P/E_Ratio                       0
P/B_Ratio                       0
dtype: int64


# check for duplicate rows
df.duplicated().sum()

0


def histogram_boxplot(data, feature, figsize=(15, 3), kde=False, bins=None):
    """
    Boxplot and histogram combined

    data: dataframe
    feature: dataframe column
    figsize: size of figure (default (15,10))
    kde: whether to show the density curve (default False)
    bins: number of bins for histogram (default None)
    """
    f2, (ax_box2, ax_hist2) = plt.subplots(
        nrows=2,  # Number of rows of the subplot grid= 2
        sharex=True,  # x-axis will be shared among all subplots
        gridspec_kw={"height_ratios": (0.25, 0.75)},
        figsize=figsize,
    )  # creating the 2 subplots
    sns.boxplot(
        data=data, x=feature, ax=ax_box2, showmeans=True, color="violet"
    )  # boxplot will be created and a triangle will indicate the mean value of the column
    sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2, bins=bins
    ) if bins else sns.histplot(
        data=data, x=feature, kde=kde, ax=ax_hist2
    )  # For histogram
    ax_hist2.axvline(
        data[feature].mean(), color="green", linestyle="--"
    )  # Add mean to the histogram
    ax_hist2.axvline(
        data[feature].median(), color="black", linestyle="-"
    )  # Add median to the histogram


# function to create labeled barplots
def labeled_barplot(data, feature, perc=False, n=None):
    """
    Barplot with percentage at the top

    data: dataframe
    feature: dataframe column
    perc: whether to display percentages instead of count (default is False)
    n: displays the top n category levels (default is None, i.e., display all levels)
    """

    total = len(data[feature])  # length of the column
    count = data[feature].nunique()
    if n is None:
        plt.figure(figsize=(count + 2, 4))
    else:
        plt.figure(figsize=(n + 2, 4))

    plt.xticks(rotation=90, fontsize=9)
    ax = sns.countplot(
        data=data,
        x=feature,
        palette="Paired",
        order=data[feature].value_counts().index[:n],
    )

    for p in ax.patches:
        if perc == True:
            label = "{:.1f}%".format(
                100 * p.get_height() / total
            )  # percentage of each class of the category
        else:
            label = p.get_height()  # count of each level of the category

        x = p.get_x() + p.get_width() / 2  # width of the plot
        y = p.get_height()  # height of the plot

        ax.annotate(
            label,
            (x, y),
            ha="center",
            va="center",
            size=12,
            xytext=(0, 5),
            textcoords="offset points",
        )  # annotate the percentage

    plt.show()  # show the plot


# Question 1. What does the distribution of stock prices look like?
histogram_boxplot(df, 'Current_Price');


data.shape

(340, 15)


# Get only numeric columns
num_cols = df.select_dtypes(include=np.number).columns.tolist()

for i in num_cols:
    histogram_boxplot(df, i, kde=True)


df.head()


# graph cdf for each numeric variable
fig, axes = plt.subplots(6, 2, figsize=(20, 20))
fig.suptitle("CDF plot of numerical variables", fontsize=20)
counter = 0
for ii in range(6):
    sns.ecdfplot(ax=axes[ii][0], x=df[num_cols[counter]])
    counter = counter + 1
    if counter != 11:
        sns.ecdfplot(ax=axes[ii][1], x=df[num_cols[counter]])
        counter = counter + 1
    else:
        pass

fig.tight_layout(pad=2.0)


# barplot on GICS Sectors
labeled_barplot(df, 'GICS_Sector', perc=True);


# barplot on GICS Sub Industry
labeled_barplot(df, 'GICS_Sub_Industry', perc=True);


sns.pairplot(df[num_cols], diag_kind='kde');


sns.catplot(data=df, x='GICS_Sector', y='Price_Change', kind='bar', height=5, aspect=1.5, ci=None)
plt.xticks(rotation=90);


# Let's look for correlation
plt.figure(figsize=(12, 5))
sns.heatmap(df[num_cols].corr(), vmin=-1, vmax=1, annot=True,cmap='Spectral', cbar=True);


sns.catplot(data=df, x='GICS_Sector', y='Cash_Ratio', kind='bar', height=4, aspect=2, ci=None)
plt.xticks(rotation=90);


sns.catplot(data=df, x='GICS_Sector', y='P/E_Ratio', kind='bar', height=4, aspect=2, ci=None)
plt.xticks(rotation=90);


# plot boxplots of all numberica columns
plt.figure(figsize=(15, 10))

for i , variable in enumerate(num_cols):
    plt.subplot(4, 4, i+1)
    plt.boxplot(df[variable], whis=1.5)
    plt.tight_layout()
    plt.title(variable)

plt.show()


# check for missing values
df.isnull().sum()

Ticker_Symbol                   0
Security                        0
GICS_Sector                     0
GICS_Sub_Industry               0
Current_Price                   0
Price_Change                    0
Volatility                      0
ROE                             0
Cash_Ratio                      0
Net_Cash_Flow                   0
Net_Income                      0
Earnings_Per_Share              0
Estimated_Shares_Outstanding    0
P/E_Ratio                       0
P/B_Ratio                       0
dtype: int64


# check for duplicate rows
df.duplicated().sum()

0


# Scale numeric columns so that we can compare columns of different labels. Using z-score.
scaler=StandardScaler()
subset=df[num_cols].copy()
subset_scaled=scaler.fit_transform(subset)


# creating a dataframe of the new scaled numeric columns
subset_scaled_df=pd.DataFrame(subset_scaled, columns=subset.columns)

# look at first 5 rows on new scaled dataframe
subset_scaled_df.head()


# explore data after manipulation
plt.figure(figsize=(12, 5))
sns.heatmap(subset_scaled_df.corr(), vmin=-1, vmax=1, annot=True,cmap='Spectral', cbar=True);


# make a copy of the scaled dataset for k-means
kmeans_subset_scaled_df = subset_scaled_df.copy()


# Elbow method to find best k-value using Euclidean distance

# run for models of 1 cluster to 10 clusters
clusters = range(1, 15)
meanDistortions = []

# Using euclidean distance, find average distortion
for k in clusters:
    model = KMeans(n_clusters=k, random_state=1)
    model.fit(kmeans_subset_scaled_df)
    prediction = model.predict(kmeans_subset_scaled_df)
    distortion = (
        sum(
            np.min(cdist(kmeans_subset_scaled_df, model.cluster_centers_, "euclidean"), axis=1)
        )
        / subset_scaled_df.shape[0]
    )

    meanDistortions.append(distortion)

    print("Number of Clusters:", k, "\tAverage Distortion:", distortion)

# graph average distortions for the cluster options
plt.plot(clusters, meanDistortions, "bx-")
plt.xlabel("k")
plt.ylabel("Average Distortion")
plt.title("Selecting k-value via the Elbow Method (Euclidean distance)", fontsize=20)

Number of Clusters: 1 	Average Distortion: 2.5425069919221697
Number of Clusters: 2 	Average Distortion: 2.382318498894466
Number of Clusters: 3 	Average Distortion: 2.269236715539074
Number of Clusters: 4 	Average Distortion: 2.175554082632614
Number of Clusters: 5 	Average Distortion: 2.136852169310594
Number of Clusters: 6 	Average Distortion: 2.0713095032364506
Number of Clusters: 7 	Average Distortion: 1.9775989987671811
Number of Clusters: 8 	Average Distortion: 1.9652603012304684
Number of Clusters: 9 	Average Distortion: 1.9311613101229936
Number of Clusters: 10 	Average Distortion: 1.8502089919483544
Number of Clusters: 11 	Average Distortion: 1.8248014803579016
Number of Clusters: 12 	Average Distortion: 1.7525839358924045
Number of Clusters: 13 	Average Distortion: 1.722563828836958
Number of Clusters: 14 	Average Distortion: 1.6925733787700508

Text(0.5, 1.0, 'Selecting k-value via the Elbow Method (Euclidean distance)')


model = KMeans(random_state=1)
visualizer = KElbowVisualizer(model, k=(1, 15), timings=True)
visualizer.fit(kmeans_subset_scaled_df)  # fit the data to the visualizer
visualizer.show();  # finalize and render figure


# finding k-value, optimal no. of clusters, with silhouette coefficients
sil_score = []
cluster_list = list(range(2, 15))
for n_clusters in cluster_list:
    clusterer = KMeans(n_clusters=n_clusters, random_state=1)
    preds = clusterer.fit_predict((kmeans_subset_scaled_df))
    # centers = clusterer.cluster_centers_
    score = silhouette_score(kmeans_subset_scaled_df, preds)
    sil_score.append(score)
    print("For n_clusters = {}, silhouette score is {}".format(n_clusters, score))

plt.plot(cluster_list, sil_score)

plt.title("Selecting k-value via Silhouette Method", fontsize=20)

For n_clusters = 2, silhouette score is 0.43969639509980457
For n_clusters = 3, silhouette score is 0.4644405674779404
For n_clusters = 4, silhouette score is 0.45434371948348606
For n_clusters = 5, silhouette score is 0.43169988466492354
For n_clusters = 6, silhouette score is 0.36698524210914063
For n_clusters = 7, silhouette score is 0.10587349598463779
For n_clusters = 8, silhouette score is 0.40021596082234684
For n_clusters = 9, silhouette score is 0.4051539505522535
For n_clusters = 10, silhouette score is 0.1147132918355368
For n_clusters = 11, silhouette score is 0.16268826704862685
For n_clusters = 12, silhouette score is 0.12883008320005324
For n_clusters = 13, silhouette score is 0.1744804713048242
For n_clusters = 14, silhouette score is 0.17457839075773943

Text(0.5, 1.0, 'Selecting k-value via Silhouette Method')


model=KMeans(random_state=1)
visualizer=KElbowVisualizer(model, k=(2,15), metric='silhouette', timings=True)
visualizer.fit(kmeans_subset_scaled_df)
visualizer.show();


# visualize the optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(3, random_state=1))
visualizer.fit(kmeans_subset_scaled_df)
visualizer.show();


# visualize the optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(4, random_state=1))
visualizer.fit(kmeans_subset_scaled_df)
visualizer.show();


# visualize the optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(6, random_state=1))
visualizer.fit(kmeans_subset_scaled_df)
visualizer.show();


# visualize the optimal no. of clusters with silhouette coefficients
visualizer = SilhouetteVisualizer(KMeans(7, random_state=1))
visualizer.fit(kmeans_subset_scaled_df)
visualizer.show();


# fit model to scaled numeric dataset
kmeans = KMeans(n_clusters=4, random_state=0)
kmeans.fit(kmeans_subset_scaled_df)

KMeans(n_clusters=4, random_state=0)

KMeans(n_clusters=4, random_state=0)


# adding kmeans cluster labels to the original dataframe & kmeans scaled df
kmeans_subset_scaled_df["K_means_cluster"] = kmeans.labels_
df["K_means_cluster"] = kmeans.labels_

# check for new column in dataset
df.head(2)


# get the numeric columns of the new dataframe
kmeans_num_cols=num_cols+["K_means_cluster"]


# group by k-value, then find the averages for each column
kmeans_cluster_profile = df[kmeans_num_cols].groupby("K_means_cluster").mean()


# add new column that tells how many items belong to each group/centroid
kmeans_cluster_profile["count_in_each_cluster"] = (
    df.groupby("K_means_cluster")["GICS_Sector"].count().values
)


# let's display cluster profiles
kmeans_cluster_profile.style.highlight_max(color="mediumaquamarine", axis=0)


# check that 'K_means_cluster' column has been added
df.head(2)


# list companies in each hierarchical cluster
for cluster in df['K_means_cluster'].unique():
    print(f"In Cluster {cluster}, the companies are:")
    print(df[df['K_means_cluster'] == cluster]['Security'].unique())
    print('-----')

In Cluster 0, the companies are:
['American Airlines Group' 'AbbVie' 'Abbott Laboratories'
 'Adobe Systems Inc' 'Archer-Daniels-Midland Co' 'Ameren Corp'
 'American Electric Power' 'AFLAC Inc'
 'American International Group, Inc.' 'Apartment Investment & Mgmt'
 'Assurant Inc' 'Arthur J. Gallagher & Co.' 'Akamai Technologies Inc'
 'Albemarle Corp' 'Alaska Air Group Inc' 'Allstate Corp' 'Allegion'
 'Applied Materials Inc' 'AMETEK Inc' 'Affiliated Managers Group Inc'
 'Ameriprise Financial' 'American Tower Corp A' 'AutoNation Inc'
 'Anthem Inc.' 'Aon plc' 'Amphenol Corp' 'Arconic Inc'
 'Activision Blizzard' 'AvalonBay Communities, Inc.' 'Broadcom'
 'American Water Works Company Inc' 'American Express Co' 'Boeing Company'
 'Baxter International Inc.' 'BB&T Corporation' 'Bard (C.R.) Inc.'
 'The Bank of New York Mellon Corp.' 'Ball Corp' 'Bristol-Myers Squibb'
 'Boston Scientific' 'BorgWarner' 'Boston Properties' 'Caterpillar Inc.'
 'Chubb Limited' 'CBRE Group' 'Crown Castle International Corp.'
 'Carnival Corp.' 'CF Industries Holdings Inc' 'Citizens Financial Group'
 'Church & Dwight' 'C. H. Robinson Worldwide' 'Charter Communications'
 'CIGNA Corp.' 'Cincinnati Financial' 'Colgate-Palmolive' 'Comerica Inc.'
 'CME Group Inc.' 'Cummins Inc.' 'CMS Energy' 'Centene Corporation'
 'CenterPoint Energy' 'Capital One Financial' 'The Cooper Companies'
 'CSX Corp.' 'CenturyLink Inc' 'Cognizant Technology Solutions'
 'Citrix Systems' 'CVS Health' 'Chevron Corp.' 'Dominion Resources'
 'Delta Air Lines' 'Du Pont (E.I.)' 'Deere & Co.'
 'Discover Financial Services' 'Quest Diagnostics' 'Danaher Corp.'
 'The Walt Disney Company' 'Discovery Communications-A'
 'Discovery Communications-C' 'Delphi Automotive' 'Digital Realty Trust'
 'Dun & Bradstreet' 'Dover Corp.' 'Dr Pepper Snapple Group' 'Duke Energy'
 'DaVita Inc.' 'eBay Inc.' 'Ecolab Inc.' 'Consolidated Edison'
 'Equifax Inc.' "Edison Int'l" 'Eastman Chemical' 'Equity Residential'
 'Eversource Energy' 'Essex Property Trust, Inc.' 'E*Trade'
 'Eaton Corporation' 'Entergy Corp.' 'Exelon Corp.' "Expeditors Int'l"
 'Expedia Inc.' 'Extra Space Storage' 'Fastenal Co'
 'Fortune Brands Home & Security' 'FirstEnergy Corp'
 'Fidelity National Information Services' 'Fiserv Inc' 'FLIR Systems'
 'Fluor Corp.' 'Flowserve Corporation' 'FMC Corporation'
 'Federal Realty Investment Trust' 'General Dynamics'
 'General Growth Properties Inc.' 'Corning Inc.' 'General Motors'
 'Genuine Parts' 'Garmin Ltd.' 'Goodyear Tire & Rubber'
 'Grainger (W.W.) Inc.' 'Hasbro Inc.' 'Huntington Bancshares'
 'HCA Holdings' 'Welltower Inc.' 'HCP Inc.' 'Hartford Financial Svc.Gp.'
 'Harley-Davidson' "Honeywell Int'l Inc." 'HP Inc.' 'Hormel Foods Corp.'
 'Henry Schein' 'Host Hotels & Resorts' 'The Hershey Company'
 'Humana Inc.' 'International Business Machines' 'IDEXX Laboratories'
 'Intl Flavors & Fragrances' 'International Paper' 'Interpublic Group'
 'Iron Mountain Incorporated' 'Illinois Tool Works' 'Invesco Ltd.'
 'J. B. Hunt Transport Services' 'Jacobs Engineering Group'
 'Juniper Networks' 'Kimco Realty' 'Kimberly-Clark' 'Kansas City Southern'
 'Leggett & Platt' 'Lennar Corp.' 'Laboratory Corp. of America Holding'
 'LKQ Corporation' 'L-3 Communications Holdings' 'Lilly (Eli) & Co.'
 'Lockheed Martin Corp.' 'Alliant Energy Corp' 'Leucadia National Corp.'
 'Southwest Airlines' 'Level 3 Communications' 'LyondellBasell'
 'Mastercard Inc.' 'Mid-America Apartments' 'Macerich' "Marriott Int'l."
 'Masco Corp.' 'Mattel Inc.' "Moody's Corp" 'Mondelez International'
 'MetLife Inc.' 'Mohawk Industries' 'Mead Johnson' 'McCormick & Co.'
 'Martin Marietta Materials' 'Marsh & McLennan' '3M Company'
 'Altria Group Inc' 'The Mosaic Company' 'Marathon Petroleum'
 'M&T Bank Corp.' 'Mettler Toledo' 'Mylan N.V.' 'Navient'
 'NASDAQ OMX Group' 'NextEra Energy' 'Newmont Mining Corp. (Hldg. Co.)'
 'Nielsen Holdings' 'Norfolk Southern Corp.' 'Northern Trust Corp.'
 'Nucor Corp.' 'Newell Brands' 'Realty Income Corporation' 'Omnicom Group'
 "O'Reilly Automotive" "People's United Financial" 'Pitney-Bowes'
 'PACCAR Inc.' 'PG&E Corp.' 'Public Serv. Enterprise Inc.' 'PepsiCo Inc.'
 'Principal Financial Group' 'Procter & Gamble' 'Progressive Corp.'
 'Pulte Homes Inc.' 'Philip Morris International' 'PNC Financial Services'
 'Pentair Ltd.' 'Pinnacle West Capital' 'PPG Industries' 'PPL Corp.'
 'Prudential Financial' 'Phillips 66' 'Praxair Inc.' 'PayPal'
 'Ryder System' 'Royal Caribbean Cruises Ltd' 'Robert Half International'
 'Roper Industries' 'Republic Services Inc' 'SCANA Corp'
 'Charles Schwab Corporation' 'Spectra Energy Corp.' 'Sealed Air'
 'Sherwin-Williams' 'SL Green Realty' 'Scripps Networks Interactive Inc.'
 'Southern Co.' 'Simon Property Group Inc' 'S&P Global, Inc.'
 'Stericycle Inc' 'Sempra Energy' 'SunTrust Banks' 'State Street Corp.'
 'Skyworks Solutions' 'Synchrony Financial' 'Stryker Corp.'
 'Molson Coors Brewing Company' 'Tegna, Inc.' 'Torchmark Corp.'
 'Thermo Fisher Scientific' 'The Travelers Companies Inc.'
 'Tractor Supply Company' 'Tyson Foods' 'Tesoro Petroleum Co.'
 'Total System Services' 'Texas Instruments' 'Under Armour'
 'United Continental Holdings' 'UDR Inc' 'Universal Health Services, Inc.'
 'United Health Group Inc.' 'Unum Group' 'Union Pacific'
 'United Parcel Service' 'United Technologies' 'Varian Medical Systems'
 'Valero Energy' 'Vulcan Materials' 'Vornado Realty Trust'
 'Verisk Analytics' 'Verisign Inc.' 'Ventas Inc' 'Wec Energy Group Inc'
 'Whirlpool Corp.' 'Waste Management Inc.' 'Western Union Co'
 'Weyerhaeuser Corp.' 'Wyndham Worldwide' 'Xcel Energy Inc' 'XL Capital'
 'Dentsply Sirona' 'Xerox Corp.' 'Xylem Inc.' 'Yum! Brands Inc'
 'Zimmer Biomet Holdings' 'Zions Bancorp' 'Zoetis']
-----
In Cluster 2, the companies are:
['Analog Devices, Inc.' 'Alliance Data Systems' 'Alexion Pharmaceuticals'
 'Amgen Inc' 'Amazon.com Inc' 'BIOGEN IDEC Inc.' 'Celgene Corp.'
 'Chipotle Mexican Grill' 'Equinix' 'Edwards Lifesciences' 'Facebook'
 'First Solar Inc' 'Frontier Communications' 'Halliburton Co.'
 'Intuitive Surgical Inc.' "McDonald's Corp." 'Monster Beverage'
 'Netflix Inc.' 'Priceline.com Inc' 'Regeneron' 'TripAdvisor'
 'Vertex Pharmaceuticals Inc' 'Waters Corporation' 'Wynn Resorts Ltd'
 'Yahoo Inc.']
-----
In Cluster 1, the companies are:
['Apache Corporation' 'Anadarko Petroleum Corp' 'Baker Hughes Inc'
 'Chesapeake Energy' 'Cabot Oil & Gas' 'Concho Resources'
 'Devon Energy Corp.' 'EOG Resources' 'EQT Corporation'
 'Freeport-McMoran Cp & Gld' 'Hess Corporation'
 'Hewlett Packard Enterprise' 'Kinder Morgan' 'Marathon Oil Corp.'
 'Murphy Oil' 'Noble Energy Inc' 'Newfield Exploration Co'
 'National Oilwell Varco Inc.' 'ONEOK' 'Occidental Petroleum'
 'Quanta Services Inc.' 'Range Resources Corp.' 'Southwestern Energy'
 'Teradata Corp.' 'Williams Cos.' 'Cimarex Energy']
-----
In Cluster 3, the companies are:
['Bank of America Corp' 'Citigroup Inc.' 'Ford Motor' 'Gilead Sciences'
 'Intel Corp.' 'JPMorgan Chase & Co.' 'Coca Cola Company' 'Merck & Co.'
 'Pfizer Inc.' 'AT&T Inc' 'Verizon Communications' 'Wells Fargo'
 'Exxon Mobil Corp.']
-----


# Listing the sectors in keach cluster
df.groupby(['K_means_cluster', 'GICS_Sector'])['Security'].count()

K_means_cluster  GICS_Sector                
0                Consumer Discretionary         33
                 Consumer Staples               17
                 Energy                          6
                 Financials                     45
                 Health Care                    28
                 Industrials                    52
                 Information Technology         24
                 Materials                      19
                 Real Estate                    26
                 Telecommunications Services     2
                 Utilities                      24
1                Energy                         22
                 Industrials                     1
                 Information Technology          2
                 Materials                       1
2                Consumer Discretionary          6
                 Consumer Staples                1
                 Energy                          1
                 Health Care                     9
                 Information Technology          6
                 Real Estate                     1
                 Telecommunications Services     1
3                Consumer Discretionary          1
                 Consumer Staples                1
                 Energy                          1
                 Financials                      4
                 Health Care                     3
                 Information Technology          1
                 Telecommunications Services     2
Name: Security, dtype: int64


plt.figure(figsize=(15, 20))
plt.suptitle("Boxplot of numerical variables for each cluster")

for i, variable in enumerate(kmeans_num_cols):
    plt.subplot(4, 3, i + 1)
    sns.boxplot(data=df, x="K_means_cluster", y=variable)

plt.tight_layout(pad=2.0)


#compare cluster vs categorical column(`GICS_Sector`)
pd.crosstab(df.K_means_cluster, df.GICS_Sector).style.highlight_max(
    color="mediumaquamarine", axis=0
)


# Graph mean values for each k-means cluster
df[kmeans_num_cols].groupby('K_means_cluster').mean().plot.bar(figsize=(12, 6), colormap='Paired');


# make a copy of the scaled dataset for k-means
hier_subset_scaled_df = subset_scaled_df.copy()


# list of distance metrics
distance_metrics = ["euclidean", "chebyshev", "mahalanobis", "cityblock"]

# list of linkage methods
linkage_methods = ["single", "complete", "average", "weighted"]

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for dm in distance_metrics:
    for lm in linkage_methods:
        Z = linkage(hier_subset_scaled_df, metric=dm, method=lm)
        c, coph_dists = cophenet(Z, pdist(hier_subset_scaled_df))
        print(
            "Cophenetic correlation for {} distance and {} linkage is {}.".format(
                dm.capitalize(), lm, c
            )
        )
        if high_cophenet_corr < c:
            high_cophenet_corr = c
            high_dm_lm[0] = dm
            high_dm_lm[1] = lm

Cophenetic correlation for Euclidean distance and single linkage is 0.9232271494002922.
Cophenetic correlation for Euclidean distance and complete linkage is 0.7873280186580672.
Cophenetic correlation for Euclidean distance and average linkage is 0.9422540609560814.
Cophenetic correlation for Euclidean distance and weighted linkage is 0.8693784298129404.
Cophenetic correlation for Chebyshev distance and single linkage is 0.9062538164750717.
Cophenetic correlation for Chebyshev distance and complete linkage is 0.598891419111242.
Cophenetic correlation for Chebyshev distance and average linkage is 0.9338265528030499.
Cophenetic correlation for Chebyshev distance and weighted linkage is 0.9127355892367.
Cophenetic correlation for Mahalanobis distance and single linkage is 0.925919553052459.
Cophenetic correlation for Mahalanobis distance and complete linkage is 0.7925307202850002.
Cophenetic correlation for Mahalanobis distance and average linkage is 0.9247324030159736.
Cophenetic correlation for Mahalanobis distance and weighted linkage is 0.8708317490180428.
Cophenetic correlation for Cityblock distance and single linkage is 0.9334186366528574.
Cophenetic correlation for Cityblock distance and complete linkage is 0.7375328863205818.
Cophenetic correlation for Cityblock distance and average linkage is 0.9302145048594667.
Cophenetic correlation for Cityblock distance and weighted linkage is 0.731045513520281.


# printing the combination of distance metric and linkage method with the highest cophenetic correlation
print(
    "Highest cophenetic correlation is {}, which is obtained with {} distance and {} linkage.".format(
        high_cophenet_corr, high_dm_lm[0].capitalize(), high_dm_lm[1]
    )
)

Highest cophenetic correlation is 0.9422540609560814, which is obtained with Euclidean distance and average linkage.


# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

high_cophenet_corr = 0
high_dm_lm = [0, 0]

for lm in linkage_methods:
    Z = linkage(hier_subset_scaled_df, metric="euclidean", method=lm)
    c, coph_dists = cophenet(Z, pdist(hier_subset_scaled_df))
    print("Cophenetic correlation for {} linkage is {}.".format(lm, c))
    if high_cophenet_corr < c:
        high_cophenet_corr = c
        high_dm_lm[0] = "euclidean"
        high_dm_lm[1] = lm

Cophenetic correlation for single linkage is 0.9232271494002922.
Cophenetic correlation for complete linkage is 0.7873280186580672.
Cophenetic correlation for average linkage is 0.9422540609560814.
Cophenetic correlation for centroid linkage is 0.9314012446828154.
Cophenetic correlation for ward linkage is 0.7101180299865353.
Cophenetic correlation for weighted linkage is 0.8693784298129404.


# printing the combination of distance metric and linkage method with the highest cophenetic correlation
print(
    "Highest cophenetic correlation is {}, which is obtained with {} linkage.".format(
        high_cophenet_corr, high_dm_lm[1]
    )
)

Highest cophenetic correlation is 0.9422540609560814, which is obtained with average linkage.


# list of linkage methods
linkage_methods = ["single", "complete", "average", "centroid", "ward", "weighted"]

# lists to save results of cophenetic correlation calculation
compare_cols = ["Linkage", "Cophenetic Coefficient"]

# to create a subplot image
fig, axs = plt.subplots(len(linkage_methods), 1, figsize=(15, 30))

# We will enumerate through the list of linkage methods above
# For each linkage method, we will plot the dendrogram and calculate the cophenetic correlation
for i, method in enumerate(linkage_methods):
    Z = linkage(hier_subset_scaled_df, metric="euclidean", method=method)

    dendrogram(Z, ax=axs[i])
    axs[i].set_title(f"Dendrogram ({method.capitalize()} Linkage)")

    coph_corr, coph_dist = cophenet(Z, pdist(hier_subset_scaled_df))
    axs[i].annotate(
        f"Cophenetic\nCorrelation\n{coph_corr:0.2f}",
        (0.80, 0.80),
        xycoords="axes fraction",
    )


# create model with cluster, distance, and linkage from above
HCmodel = AgglomerativeClustering(n_clusters=4, affinity='euclidean', linkage='ward')
HCmodel.fit(hier_subset_scaled_df)

AgglomerativeClustering(affinity='euclidean', n_clusters=4)

AgglomerativeClustering(affinity='euclidean', n_clusters=4)


# add cluster number column to original and hierarchical scaled df
hier_subset_scaled_df["HC_Clusters"] = HCmodel.labels_
df["HC_Clusters"] = HCmodel.labels_


# look at new columns in the df
df.head(2)


# get numeric columns of dataset after adding cluster column
hc_num_col = df.select_dtypes(include=np.number).columns.tolist()


# averages for each cluster by column
hc_cluster_profile = df[hc_num_col].groupby("HC_Clusters").mean()


# count how many values exist in each cluster
hc_cluster_profile["count_in_each_segments"] = (
    df.groupby("HC_Clusters")["GICS_Sector"].count().values
)


# let's display cluster profiles
hc_cluster_profile.style.highlight_max(color="mediumaquamarine", axis=0)


df.head(2)


# list companies in each hierarchical cluster
for cluster in df['HC_Clusters'].unique():
    print(f"In Cluster {cluster}, the companies are:")
    print(df[df['HC_Clusters'] == cluster]['Security'].unique())
    print('-----')

In Cluster 3, the companies are:
['American Airlines Group' 'AbbVie' 'Abbott Laboratories'
 'Adobe Systems Inc' 'Analog Devices, Inc.' 'Archer-Daniels-Midland Co'
 'Ameren Corp' 'American Electric Power' 'AFLAC Inc'
 'American International Group, Inc.' 'Apartment Investment & Mgmt'
 'Assurant Inc' 'Arthur J. Gallagher & Co.' 'Akamai Technologies Inc'
 'Albemarle Corp' 'Alaska Air Group Inc' 'Allstate Corp'
 'Applied Materials Inc' 'AMETEK Inc' 'Affiliated Managers Group Inc'
 'Ameriprise Financial' 'American Tower Corp A' 'AutoNation Inc'
 'Anthem Inc.' 'Aon plc' 'Amphenol Corp' 'Arconic Inc'
 'Activision Blizzard' 'AvalonBay Communities, Inc.' 'Broadcom'
 'American Water Works Company Inc' 'American Express Co' 'Boeing Company'
 'Baxter International Inc.' 'BB&T Corporation' 'Bard (C.R.) Inc.'
 'BIOGEN IDEC Inc.' 'The Bank of New York Mellon Corp.' 'Ball Corp'
 'Bristol-Myers Squibb' 'Boston Scientific' 'BorgWarner'
 'Boston Properties' 'Caterpillar Inc.' 'Chubb Limited' 'CBRE Group'
 'Crown Castle International Corp.' 'Carnival Corp.' 'Celgene Corp.'
 'CF Industries Holdings Inc' 'Citizens Financial Group' 'Church & Dwight'
 'C. H. Robinson Worldwide' 'CIGNA Corp.' 'Cincinnati Financial'
 'Comerica Inc.' 'CME Group Inc.' 'Cummins Inc.' 'CMS Energy'
 'Centene Corporation' 'CenterPoint Energy' 'Capital One Financial'
 'The Cooper Companies' 'CSX Corp.' 'CenturyLink Inc'
 'Cognizant Technology Solutions' 'Citrix Systems' 'CVS Health'
 'Chevron Corp.' 'Dominion Resources' 'Delta Air Lines' 'Du Pont (E.I.)'
 'Deere & Co.' 'Discover Financial Services' 'Quest Diagnostics'
 'Danaher Corp.' 'The Walt Disney Company' 'Discovery Communications-A'
 'Discovery Communications-C' 'Delphi Automotive' 'Digital Realty Trust'
 'Dun & Bradstreet' 'Dover Corp.' 'Dr Pepper Snapple Group' 'Duke Energy'
 'DaVita Inc.' 'eBay Inc.' 'Ecolab Inc.' 'Consolidated Edison'
 'Equifax Inc.' "Edison Int'l" 'Eastman Chemical' 'Equity Residential'
 'EQT Corporation' 'Eversource Energy' 'Essex Property Trust, Inc.'
 'E*Trade' 'Eaton Corporation' 'Entergy Corp.' 'Edwards Lifesciences'
 'Exelon Corp.' "Expeditors Int'l" 'Expedia Inc.' 'Extra Space Storage'
 'Fastenal Co' 'Fortune Brands Home & Security' 'FirstEnergy Corp'
 'Fidelity National Information Services' 'Fiserv Inc' 'FLIR Systems'
 'Fluor Corp.' 'Flowserve Corporation' 'FMC Corporation'
 'Federal Realty Investment Trust' 'First Solar Inc' 'General Dynamics'
 'General Growth Properties Inc.' 'Gilead Sciences' 'Corning Inc.'
 'General Motors' 'Genuine Parts' 'Garmin Ltd.' 'Goodyear Tire & Rubber'
 'Grainger (W.W.) Inc.' 'Halliburton Co.' 'Hasbro Inc.'
 'Huntington Bancshares' 'HCA Holdings' 'Welltower Inc.' 'HCP Inc.'
 'Hartford Financial Svc.Gp.' 'Harley-Davidson' "Honeywell Int'l Inc."
 'Hewlett Packard Enterprise' 'HP Inc.' 'Hormel Foods Corp.'
 'Henry Schein' 'Host Hotels & Resorts' 'The Hershey Company'
 'Humana Inc.' 'International Business Machines' 'IDEXX Laboratories'
 'Intl Flavors & Fragrances' 'International Paper' 'Interpublic Group'
 'Iron Mountain Incorporated' 'Illinois Tool Works' 'Invesco Ltd.'
 'J. B. Hunt Transport Services' 'Jacobs Engineering Group'
 'Juniper Networks' 'Kimco Realty' 'Kansas City Southern'
 'Leggett & Platt' 'Lennar Corp.' 'Laboratory Corp. of America Holding'
 'LKQ Corporation' 'L-3 Communications Holdings' 'Lilly (Eli) & Co.'
 'Lockheed Martin Corp.' 'Alliant Energy Corp' 'Leucadia National Corp.'
 'Southwest Airlines' 'Level 3 Communications' 'LyondellBasell'
 'Mastercard Inc.' 'Mid-America Apartments' 'Macerich' "Marriott Int'l."
 'Masco Corp.' 'Mattel Inc.' "McDonald's Corp." "Moody's Corp"
 'Mondelez International' 'MetLife Inc.' 'Mohawk Industries'
 'Mead Johnson' 'McCormick & Co.' 'Martin Marietta Materials'
 'Marsh & McLennan' '3M Company' 'Altria Group Inc' 'The Mosaic Company'
 'Marathon Petroleum' 'Merck & Co.' 'M&T Bank Corp.' 'Mettler Toledo'
 'Mylan N.V.' 'Navient' 'NASDAQ OMX Group' 'NextEra Energy'
 'Newmont Mining Corp. (Hldg. Co.)' 'Nielsen Holdings'
 'Norfolk Southern Corp.' 'Northern Trust Corp.' 'Nucor Corp.'
 'Newell Brands' 'Realty Income Corporation' 'Omnicom Group'
 "O'Reilly Automotive" "People's United Financial" 'Pitney-Bowes'
 'PACCAR Inc.' 'PG&E Corp.' 'Public Serv. Enterprise Inc.' 'PepsiCo Inc.'
 'Principal Financial Group' 'Procter & Gamble' 'Progressive Corp.'
 'Pulte Homes Inc.' 'Philip Morris International' 'PNC Financial Services'
 'Pentair Ltd.' 'Pinnacle West Capital' 'PPG Industries' 'PPL Corp.'
 'Prudential Financial' 'Phillips 66' 'Quanta Services Inc.'
 'Praxair Inc.' 'PayPal' 'Ryder System' 'Royal Caribbean Cruises Ltd'
 'Robert Half International' 'Roper Industries' 'Republic Services Inc'
 'SCANA Corp' 'Charles Schwab Corporation' 'Sealed Air' 'Sherwin-Williams'
 'SL Green Realty' 'Scripps Networks Interactive Inc.' 'Southern Co.'
 'Simon Property Group Inc' 'Stericycle Inc' 'Sempra Energy'
 'SunTrust Banks' 'State Street Corp.' 'Skyworks Solutions'
 'Synchrony Financial' 'Stryker Corp.' 'Molson Coors Brewing Company'
 'Tegna, Inc.' 'Torchmark Corp.' 'Thermo Fisher Scientific' 'TripAdvisor'
 'The Travelers Companies Inc.' 'Tractor Supply Company' 'Tyson Foods'
 'Tesoro Petroleum Co.' 'Total System Services' 'Texas Instruments'
 'Under Armour' 'United Continental Holdings' 'UDR Inc'
 'Universal Health Services, Inc.' 'United Health Group Inc.' 'Unum Group'
 'Union Pacific' 'United Parcel Service' 'United Technologies'
 'Varian Medical Systems' 'Valero Energy' 'Vulcan Materials'
 'Vornado Realty Trust' 'Verisk Analytics' 'Verisign Inc.'
 'Vertex Pharmaceuticals Inc' 'Ventas Inc' 'Wec Energy Group Inc'
 'Whirlpool Corp.' 'Waste Management Inc.' 'Western Union Co'
 'Weyerhaeuser Corp.' 'Wyndham Worldwide' 'Wynn Resorts Ltd'
 'Xcel Energy Inc' 'XL Capital' 'Dentsply Sirona' 'Xerox Corp.'
 'Xylem Inc.' 'Yum! Brands Inc' 'Zimmer Biomet Holdings' 'Zions Bancorp'
 'Zoetis']
-----
In Cluster 1, the companies are:
['Alliance Data Systems' 'Alexion Pharmaceuticals' 'Amgen Inc'
 'Amazon.com Inc' 'Chipotle Mexican Grill' 'Equinix' 'Facebook'
 'Frontier Communications' 'Intuitive Surgical Inc.' 'Monster Beverage'
 'Netflix Inc.' 'Priceline.com Inc' 'Regeneron' 'Waters Corporation'
 'Yahoo Inc.']
-----
In Cluster 0, the companies are:
['Allegion' 'Apache Corporation' 'Anadarko Petroleum Corp'
 'Baker Hughes Inc' 'Chesapeake Energy' 'Charter Communications'
 'Colgate-Palmolive' 'Cabot Oil & Gas' 'Concho Resources'
 'Devon Energy Corp.' 'EOG Resources' 'Freeport-McMoran Cp & Gld'
 'Hess Corporation' 'Kimberly-Clark' 'Kinder Morgan' 'Marathon Oil Corp.'
 'Murphy Oil' 'Noble Energy Inc' 'Newfield Exploration Co'
 'National Oilwell Varco Inc.' 'ONEOK' 'Occidental Petroleum'
 'Range Resources Corp.' 'Spectra Energy Corp.' 'S&P Global, Inc.'
 'Southwestern Energy' 'Teradata Corp.' 'Williams Cos.' 'Cimarex Energy']
-----
In Cluster 2, the companies are:
['Bank of America Corp' 'Citigroup Inc.' 'Ford Motor' 'Intel Corp.'
 'JPMorgan Chase & Co.' 'Coca Cola Company' 'Pfizer Inc.' 'AT&T Inc'
 'Verizon Communications' 'Wells Fargo' 'Exxon Mobil Corp.']
-----


# Listing the sectors in each cluster
df.groupby(['HC_Clusters', 'GICS_Sector'])['Security'].count()

HC_Clusters  GICS_Sector                
0            Consumer Discretionary          1
             Consumer Staples                2
             Energy                         22
             Financials                      1
             Industrials                     1
             Information Technology          1
             Materials                       1
1            Consumer Discretionary          3
             Consumer Staples                1
             Health Care                     5
             Information Technology          4
             Real Estate                     1
             Telecommunications Services     1
2            Consumer Discretionary          1
             Consumer Staples                1
             Energy                          1
             Financials                      4
             Health Care                     1
             Information Technology          1
             Telecommunications Services     2
3            Consumer Discretionary         35
             Consumer Staples               15
             Energy                          7
             Financials                     44
             Health Care                    34
             Industrials                    52
             Information Technology         27
             Materials                      19
             Real Estate                    26
             Telecommunications Services     2
             Utilities                      24
Name: Security, dtype: int64


# look at sectors in each hierarchical cluster
df.groupby(['HC_Clusters', 'GICS_Sector'])['Security'].apply(list)

HC_Clusters  GICS_Sector                
0            Consumer Discretionary                                  [Charter Communications]
             Consumer Staples                             [Colgate-Palmolive, Kimberly-Clark]
             Energy                         [Apache Corporation, Anadarko Petroleum Corp, ...
             Financials                                                    [S&P Global, Inc.]
             Industrials                                                           [Allegion]
             Information Technology                                          [Teradata Corp.]
             Materials                                            [Freeport-McMoran Cp & Gld]
1            Consumer Discretionary         [Amazon.com Inc, Chipotle Mexican Grill, Price...
             Consumer Staples                                              [Monster Beverage]
             Health Care                    [Alexion Pharmaceuticals, Amgen Inc, Intuitive...
             Information Technology         [Alliance Data Systems, Facebook, Netflix Inc....
             Real Estate                                                            [Equinix]
             Telecommunications Services                            [Frontier Communications]
2            Consumer Discretionary                                              [Ford Motor]
             Consumer Staples                                             [Coca Cola Company]
             Energy                                                       [Exxon Mobil Corp.]
             Financials                     [Bank of America Corp, Citigroup Inc., JPMorga...
             Health Care                                                        [Pfizer Inc.]
             Information Technology                                             [Intel Corp.]
             Telecommunications Services                   [AT&T Inc, Verizon Communications]
3            Consumer Discretionary         [AutoNation Inc, BorgWarner, Carnival Corp., T...
             Consumer Staples               [Archer-Daniels-Midland Co, Church & Dwight, C...
             Energy                         [Chevron Corp., EQT Corporation, Halliburton C...
             Financials                     [AFLAC Inc, American International Group, Inc....
             Health Care                    [AbbVie, Abbott Laboratories, Anthem Inc., Bax...
             Industrials                    [American Airlines Group, Alaska Air Group Inc...
             Information Technology         [Adobe Systems Inc, Analog Devices, Inc., Akam...
             Materials                      [Albemarle Corp, Ball Corp, CF Industries Hold...
             Real Estate                    [Apartment Investment & Mgmt, American Tower C...
             Telecommunications Services            [CenturyLink Inc, Level 3 Communications]
             Utilities                      [Ameren Corp, American Electric Power, America...
Name: Security, dtype: object


plt.figure(figsize=(15, 20))
plt.suptitle("Boxplot of numerical variables for each cluster")

for i, variable in enumerate(hc_num_col):
    plt.subplot(5, 3, i + 1)
    sns.boxplot(data=df, x="HC_Clusters", y=variable)

plt.tight_layout(pad=2.0)


#compare cluster vs categorical column(`GICS_Sector`)
pd.crosstab(df.HC_Clusters, df.GICS_Sector).style.highlight_max(color="mediumaquamarine", axis=0)


# Graph mean values for each hierarchical cluster
df[hc_num_col].groupby('HC_Clusters').mean().plot.bar(figsize=(12, 6), colormap='Paired');


# Total rows in each k-means cluster
kmeans_cluster_profile["count_in_each_cluster"]

K_means_cluster
0    276
1     26
2     25
3     13
Name: count_in_each_cluster, dtype: int64


# Total rows in each hierarchical cluster
hc_cluster_profile["count_in_each_segments"]

HC_Clusters
0     29
1     15
2     11
3    285
Name: count_in_each_segments, dtype: int64


# How many rows belong to a cluster in K-means and hierarchical
count = ((df['K_means_cluster'] == 0) & (df['HC_Clusters'] == 3)).sum()
print('Number of rows are shared between K-Means cluster 0 and Hierarchical cluster 3:', count)

count = ((df['K_means_cluster'] == 1) & (df['HC_Clusters'] == 1)).sum()
print('Number of rows are shared between K-Means cluster 1 and Hierarchical cluster 1:', count)

count = ((df['K_means_cluster'] == 2) & (df['HC_Clusters'] == 0)).sum()
print('Number of rows are shared between K-Means cluster 2 and Hierarchical cluster 0:', count)

count = ((df['K_means_cluster'] == 3) & (df['HC_Clusters'] == 2)).sum()
print('Number of rows are shared between K-Means cluster 3 and Hierarchical cluster 2:', count)

Number of rows are shared between K-Means cluster 0 and Hierarchical cluster 3: 270
Number of rows are shared between K-Means cluster 1 and Hierarchical cluster 1: 0
Number of rows are shared between K-Means cluster 2 and Hierarchical cluster 0: 0
Number of rows are shared between K-Means cluster 3 and Hierarchical cluster 2: 11


from sklearn.decomposition import PCA


# setting the number of compnents to 2 (2-D)
pca=PCA(n_components=2)


#transform data and storing results in a dataframe
X_reduced_pca=pca.fit_transform(subset_scaled)
reduced_df_pca=pd.DataFrame(X_reduced_pca,columns=['Component 1','Component 2'])


# checking the amount of variance explained
pca.explained_variance_ratio_.sum()

0.3712899567725855


# visualize principal component scatterplot & target variable
sns.scatterplot(data=reduced_df_pca,x='Component 1',y='Component 2');


# visualize principal component scatterplot & target variable, separated by hierarchical clusters
sns.scatterplot(data=reduced_df_pca,x='Component 1',y='Component 2', hue=df['HC_Clusters'], palette='rainbow');


# visualize principal component scatterplot & target variable, separated by hierarchical clusters
sns.scatterplot(data=reduced_df_pca,x='Component 1',y='Component 2', hue=df['K_means_cluster'], palette='rainbow');

	Ticker Symbol	Security	GICS Sector	GICS Sub Industry	Current Price	Price Change	Volatility	ROE	Cash Ratio	Net Cash Flow	Net Income	Earnings Per Share	Estimated Shares Outstanding	P/E Ratio	P/B Ratio
102	DVN	Devon Energy Corp.	Energy	Oil & Gas Exploration & Production	32.000000	-15.478079	2.923698	205	70	830000000	-14454000000	-35.55	4.065823e+08	93.089287	1.785616
125	FB	Facebook	Information Technology	Internet Software & Services	104.660004	16.224320	1.320606	8	958	592000000	3669000000	1.31	2.800763e+09	79.893133	5.884467
11	AIV	Apartment Investment & Mgmt	Real Estate	REITs	40.029999	7.578608	1.163334	15	47	21818000	248710000	1.52	1.636250e+08	26.335526	-1.269332
248	PG	Procter & Gamble	Consumer Staples	Personal Products	79.410004	10.660538	0.806056	17	129	160383000	636056000	3.28	4.913916e+08	24.070121	-2.256747
238	OXY	Occidental Petroleum	Energy	Oil & Gas Exploration & Production	67.610001	0.865287	1.589520	32	64	-588000000	-7829000000	-10.23	7.652981e+08	93.089287	3.345102
336	YUM	Yum! Brands Inc	Consumer Discretionary	Restaurants	52.516175	-8.698917	1.478877	142	27	159000000	1293000000	2.97	4.353535e+08	17.682214	-3.838260
112	EQT	EQT Corporation	Energy	Oil & Gas Exploration & Production	52.130001	-21.253771	2.364883	2	201	523803000	85171000	0.56	1.520911e+08	93.089287	9.567952
147	HAL	Halliburton Co.	Energy	Oil & Gas Equipment & Services	34.040001	-5.101751	1.966062	4	189	7786000000	-671000000	-0.79	8.493671e+08	93.089287	17.345857
89	DFS	Discover Financial Services	Financials	Consumer Finance	53.619999	3.653584	1.159897	20	99	2288000000	2297000000	5.14	4.468872e+08	10.431906	-0.375934
173	IVZ	Invesco Ltd.	Financials	Asset Management & Custody Banks	33.480000	7.067477	1.580839	12	67	412000000	968100000	2.26	4.283628e+08	14.814159	4.218620

	count	unique	top	freq	mean	std	min	25%	50%	75%	max
Ticker_Symbol	340	340	AAL	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Security	340	340	American Airlines Group	1	NaN	NaN	NaN	NaN	NaN	NaN	NaN
GICS_Sector	340	11	Industrials	53	NaN	NaN	NaN	NaN	NaN	NaN	NaN
GICS_Sub_Industry	340	104	Oil & Gas Exploration & Production	16	NaN	NaN	NaN	NaN	NaN	NaN	NaN
Current_Price	340.0	NaN	NaN	NaN	80.862345	98.055086	4.5	38.555	59.705	92.880001	1274.949951
Price_Change	340.0	NaN	NaN	NaN	4.078194	12.006338	-47.129693	-0.939484	4.819505	10.695493	55.051683
Volatility	340.0	NaN	NaN	NaN	1.525976	0.591798	0.733163	1.134878	1.385593	1.695549	4.580042
ROE	340.0	NaN	NaN	NaN	39.597059	96.547538	1.0	9.75	15.0	27.0	917.0
Cash_Ratio	340.0	NaN	NaN	NaN	70.023529	90.421331	0.0	18.0	47.0	99.0	958.0
Net_Cash_Flow	340.0	NaN	NaN	NaN	55537620.588235	1946365312.175789	-11208000000.0	-193906500.0	2098000.0	169810750.0	20764000000.0
Net_Income	340.0	NaN	NaN	NaN	1494384602.941176	3940150279.327936	-23528000000.0	352301250.0	707336000.0	1899000000.0	24442000000.0
Earnings_Per_Share	340.0	NaN	NaN	NaN	2.776662	6.587779	-61.2	1.5575	2.895	4.62	50.09
Estimated_Shares_Outstanding	340.0	NaN	NaN	NaN	577028337.75403	845849595.417695	27672156.86	158848216.1	309675137.8	573117457.325	6159292035.0
P/E_Ratio	340.0	NaN	NaN	NaN	32.612563	44.348731	2.935451	15.044653	20.819876	31.764755	528.039074
P/B_Ratio	340.0	NaN	NaN	NaN	-1.718249	13.966912	-76.119077	-4.352056	-1.06717	3.917066	129.064585

	Ticker_Symbol	Security	GICS_Sector	GICS_Sub_Industry	Current_Price	Price_Change	Volatility	ROE	Cash_Ratio	Net_Cash_Flow	Net_Income	Earnings_Per_Share	Estimated_Shares_Outstanding	P/E_Ratio	P/B_Ratio
0	AAL	American Airlines Group	Industrials	Airlines	42.349998	9.999995	1.687151	135	51	-604000000	7610000000	11.39	6.681299e+08	3.718174	-8.784219
1	ABBV	AbbVie	Health Care	Pharmaceuticals	59.240002	8.339433	2.197887	130	77	51000000	5144000000	3.15	1.633016e+09	18.806350	-8.750068
2	ABT	Abbott Laboratories	Health Care	Health Care Equipment	44.910000	11.301121	1.273646	21	67	938000000	4423000000	2.94	1.504422e+09	15.275510	-0.394171
3	ADBE	Adobe Systems Inc	Information Technology	Application Software	93.940002	13.977195	1.357679	9	180	-240840000	629551000	1.26	4.996437e+08	74.555557	4.199651
4	ADI	Analog Devices, Inc.	Information Technology	Semiconductors	55.320000	-1.827858	1.701169	14	272	315120000	696878000	0.31	2.247994e+09	178.451613	1.059810

	Current_Price	Price_Change	Volatility	ROE	Cash_Ratio	Net_Cash_Flow	Net_Income	Earnings_Per_Share	Estimated_Shares_Outstanding	P/E_Ratio	P/B_Ratio
0	-0.393341	0.493950	0.272749	0.989601	-0.210698	-0.339355	1.554415	1.309399	0.107863	-0.652487	-0.506653
1	-0.220837	0.355439	1.137045	0.937737	0.077269	-0.002335	0.927628	0.056755	1.250274	-0.311769	-0.504205
2	-0.367195	0.602479	-0.427007	-0.192905	-0.033488	0.454058	0.744371	0.024831	1.098021	-0.391502	0.094941
3	0.133567	0.825696	-0.284802	-0.317379	1.218059	-0.152497	-0.219816	-0.230563	-0.091622	0.947148	0.424333
4	-0.260874	-0.492636	0.296470	-0.265515	2.237018	0.133564	-0.202703	-0.374982	1.978399	3.293307	0.199196

	Current_Price	Price_Change	Volatility	ROE	Cash_Ratio	Net_Cash_Flow	Net_Income	Earnings_Per_Share	Estimated_Shares_Outstanding	P/E_Ratio	P/B_Ratio	count_in_each_cluster
K_means_cluster
0	72.470050	5.059104	1.388717	34.710145	52.938406	-18021028.985507	1471488521.739130	3.628424	429936522.010181	23.808921	-3.355600	276
1	35.165385	-16.390175	2.922214	110.961538	49.461538	-192318884.615385	-4041692307.692307	-9.849231	482610056.416923	78.934814	1.612922	26
2	238.072932	13.508882	1.777479	25.600000	276.280000	752195440.000000	941997320.000000	5.889600	561429538.460800	90.217183	14.211965	25
3	48.103077	6.053507	1.163964	27.538462	77.230769	773230769.230769	14114923076.923077	3.958462	3918734987.169230	16.098039	-4.253404	13

Unsupervised Learning: Trade & Ahead¶

Prepared by: Melissa Hills

Problem Statement¶

Context¶

Objective¶

Data Dictionary¶

Importing necessary libraries and data¶

Data Overview¶

Exploratory Data Analysis (EDA)¶

Univariant analysis¶

Boxplot and histogram for all numeric columns¶

CDF for numeric columns¶

GICS Sectors¶

GICS Sub Industry¶

Bivariant analysis¶

Q2.¶

Q3. Heatmap¶

Q4.¶

Q5.¶

Data Preprocessing¶

Outlier check¶

Missing value check¶

Duplicate rows¶

Scale data¶

EDA¶

K-means Clustering¶

Elbow Method¶

Now Check Silhouette Scores¶

Create Final Model¶

Cluster Profiling¶

Insights¶

Hierarchical Clustering¶

Computing Cophenetic Correlation¶

Checking Dendrograms¶

Creating Final Model Using sklearn¶

Cluster Profiling¶

Insights¶

K-means vs Hierarchical Clustering¶

Actionable Insights and Recommendations¶

Add on: PCA for visulization¶

	Current_Price	Price_Change	Volatility	ROE	Cash_Ratio	Net_Cash_Flow	Net_Income	Earnings_Per_Share	Estimated_Shares_Outstanding	P/E_Ratio	P/B_Ratio	K_means_cluster	count_in_each_segments
HC_Clusters
0	48.006208	-11.263107	2.590247	196.551724	40.275862	-495901724.137931	-3597244655.172414	-8.689655	486319827.294483	75.110924	-2.162622	0.793103	29
1	326.198218	10.563242	1.642560	14.400000	309.466667	288850666.666667	864498533.333333	7.785333	544900261.301333	113.095334	19.142151	2.000000	15
2	42.848182	6.270446	1.123547	22.727273	71.454545	558636363.636364	14631272727.272728	3.410000	4242572567.290909	15.242169	-4.924615	3.000000	11
3	72.760400	5.213307	1.427078	25.603509	60.392982	79951512.280702	1538594322.807018	3.655351	446472132.228456	24.722670	-2.647194	0.101754	285